Abstract—In this paper, source and relay precoders are derived which optimize upper and lower bounds on the Gaussian MIMO relay channel capacity. First, the prior art on the cut-set upperbound on capacity is extended by showing that the optimization of the source and relay codebooks can be formulated as a convex problem without having to introduce a scalar parameter that captures their cross-correlation. Both the Full-Duplex and Time Division Duplex (TDD) relay channels are addressed, assuming perfect knowledge of all channels, and two procedures are proposed which solve the problem efficiently by relying on analytical expressions of gradients, subgradients and projection operators: the first one solves the dual problem while the second one applies the barrier method. Similar techniques are then used to maximize the achievable rate of Decode-and-Forward (DF) TDD MIMO relaying strategies with either partial or full decoding at the relay. Sub-optimum precoders are also proposed which have a closed-form expression that can be obtained from the KKT conditions, thus reducing the computational complexity at the expense of a lower rate. Simulations in a cellular downlink scenario show that the partial DF strategy can achieve a rate very close to capacity for realistic values of the Source to Relay signalto-noise ratio. Finally, the availability of Channel State Information (CSI) in a real system is discussed.

Abstract—We consider the uplink of a backhaul-constrained, MIMO coordinated network. That is, a single-frequency network with

Many sophisticated resource allocation strategies are based on the maximization of the weighted sum of data rates for a given transmit power. While this problem can be easily solved for orthogonal multiple access schemes like TDMA, it is much more complicated if users are separated in space using multiple antennas at the base station due to the mutual coupling. In this paper, we propose a new projected conjugate gradient algorithm for the optimization of the transmit filters. The power constraint is taken into account in the calculation of the search direction by projecting the gradient onto a tangent hyperplane. Our method features excellent convergence properties when applied to dirty paper precoding, and it may also be used for the optimization of linear precoders. 1

Abstract — The maximization of a weighted sum of data rates is an essential point in cross-layer based resource allocation. Several algorithms have been proposed in the literature to solve this problem for the downlink of a multiple antenna system employing dirty paper precoding at the base station. However, they all suffer from a relatively slow convergence if the true number of objective function evaluations is taken into account. In this paper, an improved conjugate gradient method is presented, that takes the power constraint into account in the calculation of the search direction. Its superior convergence properties compared to existing approaches are verified by Monte-Carlo simulations for various scenarios. I.

We consider a backhaul-constrained coordinated cellular network. That is, a single-frequency network with N+1 multi-antenna base stations (BSs) that cooperate in order to decode the users ’ data, and that are linked by means of a common lossless backhaul, of limited capacity R. To implement receive cooperation, we propose distributed compression: N BSs, upon receiving their signals, compress them using a multisource lossy compression code. Then, they send the compressed vectors to a central BS, which performs users ’ decoding. Distributed Wyner-Ziv coding is proposed to be used, and is optimally designed in this work. The first part of the paper is devoted to a network with a unique multi-antenna user, that transmits a predefined Gaussian space-time codeword. For such a scenario, the compression codebooks at the BSs are optimized, considering the user’s achievable rate as the performance metric. In particular, for N = 1 the optimum codebook distribution is derived in closed form, while for N> 1 an iterative algorithm is devised. The second part of the contribution focusses on the multi-user scenario. For it, the achievable rate region is obtained by means of the optimum compression codebooks for sum-rate and weighted sum-rate, respectively.